{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:49:24Z","timestamp":1782499764998,"version":"3.54.5"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032299239","type":"print"},{"value":"9783032299246","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-29924-6_20","type":"book-chapter","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:09:05Z","timestamp":1782497345000},"page":"272-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Aligning and\u00a0Assimilating Multi-source Data for\u00a0Flood Forecasting"],"prefix":"10.1007","author":[{"given":"Kun","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Bertoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibo","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Schr\u00f6ter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrica","family":"Caporali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew D.","family":"Piggott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanghua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rossella","family":"Arcucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.advwatres.2017.09.026","volume":"110","author":"R Alvarado-Montero","year":"2017","unstructured":"Alvarado-Montero, R., Schwanenberg, D., Krahe, P., Helmke, P., Klein, B.: Multi-parametric variational data assimilation for hydrological forecasting. Adv. Water Resour. 110, 182\u2013192 (2017)","journal-title":"Adv. Water Resour."},{"issue":"1","key":"20_CR2","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s11069-008-9277-8","volume":"49","author":"H Apel","year":"2009","unstructured":"Apel, H., Aronica, G.T., Kreibich, H., Thieken, A.H.: Flood risk analyses\u2013how detailed do we need to be? Nat. Hazards 49(1), 79\u201398 (2009)","journal-title":"Nat. Hazards"},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.jcp.2018.10.042","volume":"379","author":"R Arcucci","year":"2019","unstructured":"Arcucci, R., Mottet, L., Pain, C., Guo, Y.K.: Optimal reduced space for variational data assimilation. J. Comput. Phys. 379, 51\u201369 (2019)","journal-title":"J. Comput. Phys."},{"key":"20_CR4","unstructured":"Bertoli, G., Schroeter, K., Arcucci, R., Caporali, E.: A hybrid machine learning framework for improved short-term peak-flow forecasting. arXiv preprint arXiv:2601.09336 (2026)"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Boucher, M.A., Quilty, J., Adamowski, J.: Data assimilation for streamflow forecasting using extreme learning machines and multilayer perceptrons. Water Resources Res. 56(6), e2019WR026226 (2020)","DOI":"10.1029\/2019WR026226"},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1038\/s41597-019-0326-9","volume":"6","author":"JA de Bruijn","year":"2019","unstructured":"de Bruijn, J.A., de Moel, H., Jongman, B., de Ruiter, M.C., Wagemaker, J., Aerts, J.C.: A global database of historic and real-time flood events based on social media. Sci. Data 6(1), 311 (2019)","journal-title":"Sci. Data"},{"issue":"13","key":"20_CR7","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.3390\/w16131763","volume":"16","author":"N Byaruhanga","year":"2024","unstructured":"Byaruhanga, N., Kibirige, D., Gokool, S., Mkhonta, G.: Evolution of flood prediction and forecasting models for flood early warning systems: a scoping review. Water 16(13), 1763 (2024)","journal-title":"Water"},{"issue":"1","key":"20_CR8","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1002\/2016WR019208","volume":"53","author":"G Ercolani","year":"2017","unstructured":"Ercolani, G., Castelli, F.: Variational assimilation of streamflow data in distributed flood forecasting. Water Resour. Res. 53(1), 158\u2013183 (2017)","journal-title":"Water Resour. Res."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Fry, M., Swain, O.: Hydrological data management systems within a national river flow archive (2010)","DOI":"10.7558\/bhs.2010.ic118"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Ghorpade, P., et al.: Flood forecasting using machine learning: a review. In: 2021 8th International Conference on Smart Computing and Communications (ICSCC), pp. 32\u201336. IEEE (2021)","DOI":"10.1109\/ICSCC51209.2021.9528099"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Grimaldi, S., et al.: River discharge and related historical data from the global flood awareness system, v4.0 (2022). https:\/\/doi.org\/10.24381\/cds.a4fdd6b9. Accessed 22 Sept 2025","DOI":"10.24381\/cds.a4fdd6b9"},{"issue":"9","key":"20_CR12","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1038\/nclimate1911","volume":"3","author":"Y Hirabayashi","year":"2013","unstructured":"Hirabayashi, Y., et al.: Global flood risk under climate change. Nat. Clim. Chang. 3(9), 816\u2013821 (2013)","journal-title":"Nat. Clim. Chang."},{"issue":"3","key":"20_CR13","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/15715124.2017.1411920","volume":"16","author":"SK Jain","year":"2018","unstructured":"Jain, S.K., et al.: A brief review of flood forecasting techniques and their applications. Int. J. River Basin Manag. 16(3), 329\u2013344 (2018)","journal-title":"Int. J. River Basin Manag."},{"key":"20_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.131304","volume":"636","author":"M Jeong","year":"2024","unstructured":"Jeong, M., Kwon, M., Cha, J.H., Kim, D.H.: High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation. J. Hydrol. 636, 131304 (2024)","journal-title":"J. Hydrol."},{"key":"20_CR15","doi-asserted-by":"publisher","unstructured":"Klingler, C., Schulz, K., Herrnegger, M.: Lamah-ce: large-sample data for hydrology and environmental sciences for central Europe. Earth Syst. Sci. Data 13(9), 4529\u20134565 (2021). https:\/\/doi.org\/10.5194\/essd-13-4529-2021. https:\/\/essd.copernicus.org\/articles\/13\/4529\/2021\/","DOI":"10.5194\/essd-13-4529-2021"},{"issue":"11","key":"20_CR16","doi-asserted-by":"publisher","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","volume":"22","author":"F Kratzert","year":"2018","unstructured":"Kratzert, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M.: Rainfall-runoff modelling using long short-term memory (LSTM) networks. Hydrol. Earth Syst. Sci. 22(11), 6005\u20136022 (2018)","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"12","key":"20_CR17","doi-asserted-by":"publisher","first-page":"5089","DOI":"10.5194\/hess-23-5089-2019","volume":"23","author":"F Kratzert","year":"2019","unstructured":"Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol. Earth Syst. Sci. 23(12), 5089\u20135110 (2019)","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"11","key":"20_CR18","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.5194\/hess-18-4325-2014","volume":"18","author":"X Lai","year":"2014","unstructured":"Lai, X., Liang, Q., Yesou, H., Daillet, S.: Variational assimilation of remotely sensed flood extents using a 2-D flood model. Hydrol. Earth Syst. Sci. 18(11), 4325\u20134339 (2014)","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"20_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2024.102523","volume":"85","author":"J Lever","year":"2025","unstructured":"Lever, J., et al.: Facing & mitigating common challenges when working with real-world data: the data learning paradigm. J. Comput. Sci. 85, 102523 (2025)","journal-title":"J. Comput. Sci."},{"issue":"5","key":"20_CR20","doi-asserted-by":"publisher","first-page":"973","DOI":"10.2166\/hydro.2013.075","volume":"16","author":"XL Li","year":"2014","unstructured":"Li, X.L., L\u00fc, H., Horton, R., An, T., Yu, Z.: Real-time flood forecast using the coupling support vector machine and data assimilation method. J. Hydroinf. 16(5), 973\u2013988 (2014)","journal-title":"J. Hydroinf."},{"issue":"9","key":"20_CR21","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.3390\/w15091716","volume":"15","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Liu, J., Li, C., Liu, L., Wang, Y.: A WRF\/WRF-hydro coupled forecasting system with real-time precipitation-runoff updating based on 3dvar data assimilation and deep learning. Water 15(9), 1716 (2023)","journal-title":"Water"},{"issue":"11","key":"20_CR22","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.3390\/rs13112103","volume":"13","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Liu, J., Li, C., Yu, F., Wang, W.: Effect of the assimilation frequency of radar reflectivity on rain storm prediction by using WRF-3DVAR. Remote Sens. 13(11), 2103 (2021)","journal-title":"Remote Sens."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Maspo, N.A., Bin\u00a0Harun, A.N., Goto, M., Cheros, F., Haron, N.A., Mohd\u00a0Nawi, M.N.: Evaluation of machine learning approach in flood prediction scenarios and its input parameters: a systematic review. In: IOP Conference Series: Earth and Environmental Science, vol.\u00a0479, p. 012038. IOP Publishing (2020)","DOI":"10.1088\/1755-1315\/479\/1\/012038"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Ozturk, P., Chau, K.W.: Flood prediction using machine learning models: literature review. Water 10(11), 1536 (2018)","DOI":"10.3390\/w10111536"},{"issue":"1\u20132","key":"20_CR25","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/S0022-1694(02)00135-X","volume":"267","author":"EJ Plate","year":"2002","unstructured":"Plate, E.J.: Flood risk and flood management. J. Hydrol. 267(1\u20132), 2\u201311 (2002)","journal-title":"J. Hydrol."},{"key":"20_CR26","unstructured":"SShi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Smith, P., et al.: On the operational implementation of the European flood awareness system (EFAS). In: Flood Forecasting, pp. 313\u2013348. Elsevier (2016)","DOI":"10.1016\/B978-0-12-801884-2.00011-6"},{"key":"20_CR28","doi-asserted-by":"publisher","unstructured":"Thiemig, V., et al.: Emo-5: a high-resolution multi-variable gridded meteorological dataset for Europe. Earth Syst. Sci. Data 14(7), 3249\u20133272 (2022). https:\/\/doi.org\/10.5194\/essd-14-3249-2022. https:\/\/essd.copernicus.org\/articles\/14\/3249\/2022\/","DOI":"10.5194\/essd-14-3249-2022"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: AI-empowered latent four-dimensional variational data assimilation for river discharge forecasting. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2025)","DOI":"10.1109\/JSTARS.2025.3611136"},{"key":"20_CR30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-031-97567-7_4","volume-title":"Computational Science - ICCS 2025 Workshops","author":"K Wang","year":"2025","unstructured":"Wang, K., et al.: Latent three-dimensional variational data assimilation with convolutional autoencoder and LSTM for flood forecasting. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds.) Computational Science - ICCS 2025 Workshops, pp. 43\u201356. Springer, Cham (2025)"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: Latent three-dimensional variational data assimilation with convolutional autoencoder and LSTM for flood forecasting. In: International Conference on Computational Science, pp. 43\u201356. Springer (2025)","DOI":"10.1007\/978-3-031-97567-7_4"},{"issue":"772","key":"20_CR32","doi-asserted-by":"publisher","DOI":"10.1002\/qj.5009","volume":"151","author":"K Wang","year":"2025","unstructured":"Wang, K., Cheng, S., Piggott, M.D., Dance, S.L., Wang, Y., Arcucci, R.: Latent data assimilation with non-explicit observation operator in hydrology. Q. J. R. Meteorol. Soc. 151(772), e5009 (2025)","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Wang, K., D.\u00a0Piggott, M., Wang, Y., Arcucci, R.: Neural network as transformation function in data assimilation. In: International Conference on Computational Science, pp. 322\u2013329. Springer (2024)","DOI":"10.1007\/978-3-031-63775-9_23"},{"key":"20_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.atmosres.2016.07.026","volume":"183","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Min, J., Chen, Y., Huang, X.Y., Zeng, M., Li, X.: Improving precipitation forecast with hybrid 3dvar and time-lagged ensembles in a heavy rainfall event. Atmos. Res. 183, 1\u201316 (2017)","journal-title":"Atmos. Res."},{"issue":"1","key":"20_CR35","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1038\/s41597-025-04725-2","volume":"12","author":"Q Xu","year":"2025","unstructured":"Xu, Q., Shi, Y., Zhao, J., Zhu, X.X.: Floodcastbench: a large-scale dataset and foundation models for flood modeling and forecasting. Sci. Data 12(1), 431 (2025)","journal-title":"Sci. Data"},{"issue":"3","key":"20_CR36","doi-asserted-by":"publisher","first-page":"65","DOI":"10.51526\/kbes.2023.4.3.65-103","volume":"4","author":"ZM Yaseen","year":"2023","unstructured":"Yaseen, Z.M.: A new benchmark on machine learning methodologies for hydrological processes modelling: a comprehensive review for limitations and future research directions. Knowl.-Based Eng. Sci. 4(3), 65\u2013103 (2023)","journal-title":"Knowl.-Based Eng. Sci."},{"issue":"10","key":"20_CR37","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.3390\/w16101407","volume":"16","author":"X Zhao","year":"2024","unstructured":"Zhao, X., et al.: A comprehensive review of methods for hydrological forecasting based on deep learning. Water 16(10), 1407 (2024)","journal-title":"Water"},{"key":"20_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.123924","volume":"577","author":"MG Ziliani","year":"2019","unstructured":"Ziliani, M.G., Ghostine, R., Ait-El-Fquih, B., McCabe, M.F., Hoteit, I.: Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation. J. Hydrol. 577, 123924 (2019)","journal-title":"J. Hydrol."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29924-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:09:11Z","timestamp":1782497351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29924-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032299239","9783032299246"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29924-6_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"27 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hamburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2026\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}